Hyperspectral imaging is the process of using specialized sensors to collect image information across the electromagnetic spectrum (unlike the human eye, which just sees visible light). Objects have their own respective fingerprints known as “spectral signatures” which effectuate the identification of the materials that make up the object. For example, the spectral signature for oil helps mineralogists locate oil fields. Hyperspectral imaging (as opposed to multi-spectral imaging) deals with imaging narrow contiguous spectral bands over a large spectral range, and produces the spectra of all pixels in the captured scene. A sensor with only 20 bands can also be hyperspectral when it covers a substantial spectral range (for example, from 500 nm to 700 nm with 20 10 nm wide bands). Whereas, a sensor with 20 discrete bands covering the same range whose wavelengths are not continuous and cannot be assigned specifically (for example, a 7 channel Flux Data camera, FD-1665) due to the use of multiple broad band filters would be considered multispectral. The primary advantages to hyperspectral imaging is that, because an entire spectrum is acquired at each point, and the wavelengths are known, the operator needs no prior knowledge of the sample, and post-processing allows all available information from the dataset to be mined. Disadvantages are cost and complexity as high-speed computers, very sensitive detection equipment, and large storage capacities, are often required for analyzing hyperspectral data. Data storage capacity is significant since hyperspectral image/data cubes are large multi-dimensional datasets. All of these factors greatly increase the cost of acquiring and processing hyperspectral data. The acquisition and processing of hyperspectral images is also referred to as ‘imaging spectroscopy’. In an increasing variety of diverse applications, there is a need to capture two dimensional images of a scene or object of interest and decompose the captured image into its spectral bands such that objects in the image can be identified.
Accordingly, what is needed are increasingly sophisticated systems and methods for analyzing a hyperspectral image taken by a hyperspectral camera and classifying the pixels in that image such that an object captured by the camera system can be identified in remote non-invasive sensing applications.